LLM trace storage: how to store and search production agent runs

Production LLM traces capture an application request as a tree of connected spans, preserving model calls, tool executions, prompts, outputs, identifiers, and metadata needed to reproduce and investigate failures. Because agent traces can contain large prompts, retrieved documents, and tool outputs, storage systems must balance completeness, cost, searchability, and retention; truncation and sampling can reduce volume but may remove critical evidence, while separating indexed metadata from accessible attachments can preserve large payloads. Effective investigations require full-text search, structured span filters, consistent application metadata, and the ability to distinguish conditions occurring within the same span from those occurring elsewhere in a trace. Organizations can store raw records in object storage, but doing so requires additional indexing, query, and trace-reconstruction tools, whereas dedicated trace backends provide integrated search and inspection, often alongside archival exports. Evaluations should test complete trace retrieval, search speed, large-payload handling, retention boundaries, exports, indexing freshness, and access controls using realistic production workloads. Braintrust presents Brainstore as a trace database built on object storage with indexing for semi-structured AI data, supporting trace search, attachments, configurable retention, exports, role-based access, and visual inspection of span trees, timelines, conversations, and raw records.

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